System and method for power effective participatory sensing

ABSTRACT

Disclosed is a method and system enabling power effective participatory sensing. The hand held device of the system is equipped with plurality of sensors, and is configured to enable the power effective sensor to monitor operation of the power intensive sensors. In one embodiment, a participatory sensing approach is used for traffic condition. A methodology for triggering power hungry sensors (audio) with the help of low power sensors (accelerometer) is presented which is able to reduce the overall power consumption of the mobile device. Further, a decision tree based approach is used to classify the level of congestion by measuring the horn density in a particular location.

TECHNICAL FIELD

The present subject matter described herein, in general, relates toparticipatory sensing, and more particularly to a power effectiveparticipatory sensing approach in hand held devices.

BACKGROUND

Participatory Sensing is an idea of the society (the groups of people)for providing sensory information. The idea of participatory sensingarises from the improvement of sensor enabled mobile phones in the lastfew years, which made participatory sensing feasible on a large-scale.The present mobile phone apart from their conventional communicationfacilities, are equipped with a variety of sensors like accelerometer,gyroscope, Global Position System (GPS) etc. Thus by collecting andaggregating data from plurality of phones sensors in a similar location,important estimations on road, traffic congestion, weather conditions,ecological information, and any other sensory information can beobtained. This approach resolves the problem of installing andmonitoring roadside sensors. Moreover, decision making can be done byreading several sensors, which makes the approach more reliable.

The challenge in participatory sensing lies in the optimum and effectiveutilization of mobile phone sensors. In order to make effectiveutilization of mobile phone sensors, the fact that the applications useminimum mobile phone resources, must be taken into consideration. Theunsafe collection of information poses further challenges to theauthenticity of transmitted information. Individual sensors may requirea trusted platform or hierarchical trust structures. Additionalchallenges include, but are not limited to, security, and privacy.

In traditional approach for monitoring traffic conditions, participatorysensing based solution for traffic condition monitoring uses the inbuiltsensors like microphone sensor, accelerometer sensor, GPS sensors etc.,of users' mobile phones for traffic condition monitoring. The majorproblem in these participatory sensing approaches is that some sensorslike microphone or GPS consumes huge amount of battery power. Theconventional systems do not pay attention to the power consumption issueof mobile phones.

Traditionally, the general power consumptions of the mobile phonesensors have been estimated, from which it can be inferred that sensorslike microphones and GPS consume huge amount of battery power of themobile device. Accelerometer, on the other hand consumes very lesspower. Thus, if some logic is derived to turn on and off the high powerconsuming sensors using continuous monitoring of the low power consumingsensors, then the overall power consumption of the system can besignificantly reduced.

SUMMARY

This summary is provided to introduce aspects related to systems andmethods for power effective participatory sensing and the aspects arefurther described below in the detailed description. This summary is notintended to identify essential features of the claimed subject matternor is it intended for use in determining or limiting the scope of theclaimed subject matter.

In one implementation, a participatory sensing system enabling at leastone power effective sensor to monitor operation of one or more powerintensive sensors is disclosed. The participatory sensing systemcomprises a hand held device, at least one power effective sensor, oneor more power intensive sensors, and a backend server. The hand helddevice is equipped with plurality of sensors, and configured to enablethe power effective sensor to monitor operation of the power intensivesensors. The power effective sensor remains in an active state tocapture and analyze contextual information related to an object area.The analyzed contextual information received from the power effectivesensor triggers the one or more power intensive sensors for audiorecording. At least one feature from the audio recording is extractedand concurrently a corresponding metadata file is generated. A backendserver receives the features and the metadata file from the hand helddevice which is further mapped with the loaded training models fortracking and monitoring the object area condition.

In one implementation, an energy efficient method for participatorysensing for enabling at least one power effective sensor to monitoroperation of one or more power intensive sensors is disclosed. The powereffective sensor remains in an active state to capture and analyzecontextual information related to an object area. In response toreceiving analyzed contextual information from the power effectivesensor, the one or more power intensive sensors are triggered toinitiate audio recording for extracting at least one feature therefrom,and concurrently generate a corresponding metadata file; and thefeatures and the metadata file are then transmitted for analysis, to abackend server, wherein said features and the metadata file are analyzedfor tracking the object area condition.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Thesame numbers are used throughout the drawings to refer like features andcomponents.

FIG. 1 illustrates a participatory sensing system, in accordance with anembodiment of the present subject matter.

FIG. 2 illustrates a block diagram of processing at users' mobile phone,in accordance with an embodiment of the present subject matter.

FIG. 3 illustrates a block diagram of processing at a backend server, inaccordance with an embodiment of the present subject matter.

FIG. 4 illustrates a decision tree for traffic condition monitoring, inaccordance with an exemplary embodiment of the present subject matter.

FIG. 5 illustrates a participatory sensing method, in accordance with anembodiment of the present subject matter.

DETAILED DESCRIPTION

Systems and methods an energy efficient method for participatory sensingfor enabling at least one power effective sensor to monitor operation ofone or more power intensive sensors are described. The present subjectmatter discloses effective and efficient mechanism for triggered sensingbased approach wherein at least one power effective sensor is always onand which turns on one or more power intensive sensors for capturingcontextual information related to an object area when some triggeringevent occur that needs to be recorded. The participatory sensing systemuses an accelerometer, orientation sensor and the like, or a combinationthereof as the power effective sensors; and microphones, GlobalPositioning System (GPS); gyroscope, location providing sensors and thelike, or a combination thereof as the power intensive sensors. Thecontextual information captured includes location based information,timestamp information, traffic conditions, weather conditions, areaestimations and the like. The power effective sensor analyses thecontextual information. For example, the contextual information can beanalyzed based on vehicular static or dynamic state interpreted from anaccelerometer data, vehicle-honking rate or a combination thereof.

Based on the analyzed contextual information the system triggers the oneor more power intensive sensors for audio recording in the object areaor area in the close proximity of the object area. Further, at least onefeature from the audio recording is extracted and a correspondingmetadata file is concurrently generated. For example, a decision treebased approach is used for classifying at least one feature from theaudio recording by measuring the audio density in a particular area.

While aspects of described system and method for power effectiveparticipatory sensing may be implemented in any number of differentcomputing systems, environments, and/or configurations, the embodimentsare described in the context of the following exemplary system.

Referring now to FIG. 1, the participatory sensing system for enablingefficient power utilization is disclosed. The participatory sensingsystem comprises a hand held device 102, at least one power effectivesensor 104, one or more power intensive sensors, 106, and a backendserver 108. The backed server 108 further comprises of one or morepreloaded training models 110, and a mapping module 112. The hand helddevice 102 is equipped with plurality of sensors, and configured toenable the power effective sensor 104 to monitor operation of the powerintensive sensors 106. The power effective sensors 104 remains in anactive state to capture and analyze contextual information related to anobject area. The analyzed contextual information received from the powereffective sensor 104 triggers the one or more power intensive sensors106 for audio recording. At least one feature from the audio recordingis extracted and concurrently a corresponding metadata file isgenerated. In one alternate embodiment, the hand held device 102compresses the features and the metadata file before transmitting to thebackend server 108 to reduce the data rate. The backend server 108receives the feature and the metadata file in compressed format from thehand held device, which is further decompressed and mapped with theloaded training models 110 for tracking and monitoring the object areacondition.

Although the present subject matter is explained considering that theparticipatory sensing system 100 comprises of the hand held device 102,it may be understood that the hand held device 102 may also be put intopractice in a variety of computing systems, such as a laptop computer, adesktop computer, a notebook, a workstation, a mainframe computer, aserver, a network server, and the like. Examples of the hand held device102 may include, but are not limited to, a portable computer, a personaldigital assistant, a handheld device, and a workstation. The hand helddevice 102 is communicatively coupled to the backend server 108 througha network 118.

In one implementation, the network may be a wireless network, a wirednetwork or a combination thereof. The network 118 can be implemented asone of the different types of networks, such as intranet, local areanetwork (LAN), wide area network (WAN), the internet, and the like. Thenetwork 118 may either be a dedicated network or a shared network. Theshared network represents an association of the different types ofnetworks that use a variety of protocols, for example, HypertextTransfer Protocol (HTTP), Transmission Control Protocol/InternetProtocol (TCP/IP), Wireless Application Protocol (WAP), and the like, tocommunicate with one another. Further the network 106 may include avariety of network devices, including routers, bridges, servers,computing devices, storage devices, and the like.

The hand held device 102 is equipped with plurality of sensors. Therecan be the plurality of the power effective sensor 104 to monitor theoperation of the plurality of power intensive sensors 106. In oneexample, the power effective sensors 104 may include an accelerometer,orientation sensor and the like, or a combination thereof. The powerintensive sensors 106 may include microphones; location providingsensors such as Global Positioning System (GPS); gyroscope and the like,or a combination thereof.

The power effective sensors 104 remains in an active state to capturethe contextual information related to an object area. In one example,the contextual information includes the location based information,timestamp information, traffic conditions, weather conditions, areaestimations and the like. The location based information is capturedusing received signal strength indicator (RSSI) signals sensed from oneor more hand held devices 102 present within or in close proximity ofthe object area. The contextual information captured is analyzed by thepower effective sensors 104. In one example, the power effective sensor104 analyses the contextual information based on vehicular static ordynamic state interpreted from an accelerometer data, andvehicle-honking rate.

In response to analyzed contextual information, the power effectivesensor 104 further triggers the one or more power intensive sensors 106for audio recording. The power effective sensor 104 may trigger the oneor more power intensive sensors 106 for audio recording based on thevariance of the data collected from the power effective sensor 104 andcompare against a threshold limit prior to triggering of the powerintensive sensors into an active state. In one example the variance ofthe data collected from the power effective sensor 104 is the varianceof the resultant accelerometer. If the 3-axis accelerometer on mobiledevice captures samples in all the three axes. The resultantaccelerometer is calculated using equation 1, below.

â _(res)=√{square root over ({circumflex over (x)} ² +ŷ ² +{circumflexover (z)} ²)}  (1)

The variance (V) of the accelerometer is computed using equation 2,below.

$\begin{matrix}{V = \frac{\sum\limits_{k = 1}^{N}\left( {a_{resK} - {\overset{\_}{a}}_{res}} \right)^{2}}{N - 1}} & (2)\end{matrix}$

where, N is equal to the number of samples chosen for analysis andā_(res) is the mean acceleration value.

In one example the lower threshold value is considered as 0.5 and theupper threshold value is considered as 1.2.

The audio recording may include spoken voice, singing, instrumentalmusic, or sound effects. The power intensive sensor 106 further extractsat least one feature from the audio recording audio recording. In oneexample, the power intensive sensors 106 accomplishes feature extractionby using a modified Mel Frequency Cepstral Coefficient (MFCC) obtainedby combining conventional Mel filter structure and reverse structure ofconventional Mel filter. In one example, the audio feature may includecepstral coefficients, zero crossing rate, spectral power roll off etc.The power intensive sensors 106 further generate a metadata file. In oneexample, the metadata file may be an XML metadata file containing theinformation of the power effective sensor 104 and the contextualinformation. The contextual information may include location basedinformation, timestamp information, traffic conditions, weatherconditions, area estimations and the like. The location basedinformation is captured using received signal strength indicator (RSSI)signals sensed from one or more hand held devices present within or inclose proximity of the object area.

The hand held device 102 further compresses the features andcorresponding metadata file for transmitting to the backend server 108for analysis, using the network 118. The analysis enables tracking theobject area condition. The backend server 108 decompresses thecompressed features and the corresponding metadata file obtained fromthe hand held device 112. The decompressed features and thecorresponding metadata file are stored in the mapping modulerespectively. The backend server 108 is loaded with one or more trainingmodels 110. In one example, the training models 110 are created using aGaussian Mixture Model classifiers based on audio analysis of horn andnon horn sound detected from the object area. The non horn soundincludes non vehicular noise sound emanating from engine idle noise,tire noise, air turbulence noise, human speech, vehicle music noise andthe like.

The analysis at the backend server involves, mapping the features andthe corresponding metadata file with one or more pre-stored trainingmodels 110 of the backend server that enables tracking the status andcondition of the object area. In one example, tracking the status andcondition of the object area may be used to display the traffic density(low/high) of the particular object area along with the latitude andlongitude of the area and may be visualized on a Google Map applicationon users' mobile device 102.

In one implementation, the object area condition refers to trafficcondition.

Referring now to FIG. 2, the block diagram of processing at users'mobile phone 200, in accordance with an exemplary embodiment of thepresent subject matter.

In one implementation, the processing at the users' mobile end isexplained as an exemplary embodiment of the present invention whereinthe object area condition refers to a traffic condition in an objectarea. The power effective sensors like accelerometer capture the dynamicstate data of a vehicle. The data is sampled and aggregated in realtime. A location manager application may be used to tracks the locationinformation based on wireless network of the phone. The accelerometerdata is computed and compared against a threshold limit prior to triggeron the capturing of audio recording by the power intensive sensors. Inone example the lower threshold value is considered as 0.5 and the upperthreshold value is considered as 1.2. Feature extraction is donesimultaneously on the recorded audio in real time. During the process ofaudio recording a metadata xml file is generated which contains locationinformation and time related information. The location information maybe derived from the RSSI information using Android ApplicationProgramming Interface (API). Audio feature data is compressed to reducedata rate. In one example, the compressed feature and correspondingmetadata file are locally stored in a queue. There exists a backgroundprocess that listens to the queue and posts the compressed feature dataand xml file to a backend server 108 for further processing.

Referring now to FIG. 3, the block diagram of processing 300 at thebackend server 108, in accordance with an embodiment of the presentsubject matter.

In one implementation, the processing at the backend server 108 isexplained as an exemplary embodiment of the present invention, whereinthe object area condition refers to traffic condition. The backendserver 108 is mainly responsible for audio processing for horn detectionand for combining the results with the xml metadata file entries. Abackground process always runs in the server that receives thecompressed feature data and xml files arriving from mobile devices, asdiscussed in FIG. 3. After Receiving, the compressed feature data isdecompressed. Training models for horn sounds and other traffic soundsare already loaded in the server. This process may be off-line and doneonly once. Extracted features are compared with the training models anddecision making is done based on maximum probability score model. In oneexample, the decision making may be performed using Gaussian MixtureModels (GMM) algorithm. The training models are created using GMM. Thus,two models are created, one for horn sound and the other for non-hornsound. During the recognition phase, the decision-making is done basedon maximum probability score with respect to the training models for thetest samples. The XML files received are decompressed and parsed foranalyzing the data preset in the file. Further, the audio comparingresult is combined with the xml metadata entries and decision making isdone by collecting results from many sensors in similar location usingfusion. The decision making is discussed in detail, in FIG. 4. Based onthe results the traffic conditions may be obtained using navigationapplication in smart hand held devices. In one example, the navigationapplication may include a “Google Map”, which is available to users forpublic use. The traffic condition may be periodically updated in thebackend server 108.

Referring now to FIG. 4, the decision tree for traffic conditionmonitoring 400, in accordance with an embodiment of the present subjectmatter.

In one implementation, the decision tree for traffic conditionmonitoring is explained as an exemplary embodiment of the presentinvention, wherein the object area condition refers to trafficcondition. The decision making is for vehicle traffic congestioncondition monitoring. The variance of accelerometer data is firstanalyzed. Analysis is performed each sample obtained by calculating thevariance of the absolute magnitude of the accelerometer reading. Fromthe results, it is observed that, when the vehicle is in rest, thevariance of accelerometer data is very less. However when the vehicle isin motion, the variance of accelerometer data becomes high. The reasonlies in the fact that both velocity and acceleration of a vehicle inmotion is fixed and they change abruptly for changing lanes, pullingbrakes etc. Based on the above facts, the participatory sensing system100 provides the decision tree based approach for traffic conditionmonitoring, is as shown in FIG. 4. The system 100 performs the stepslike calculating the variance of accelerometer sample data, in oneexample. As shown in FIG. 4, if the value is below a predeterminedthreshold value L1, it triggers the audio recording; feature extractionand metadata file creation. In one example the lower threshold value isconsidered as 0.5 and the upper threshold value is considered as 1.2. Ifthe variance value is below L1, there can be two cases—the vehicle iseither in rest or is moving with uniform velocity. The above two casesare separated out by observing the geo locations. If the vehicle is inmotion there is a high possibility that two geo locations will not bethe same. Further analysis is done on audio features where the vehicleis in rest to derive the traffic condition.

Referring now to FIG. 5, the participatory sensing method for trackingand monitoring of the objects in an area condition (500), in accordancewith an embodiment of the present subject matter.

At block 502, the power effective sensor in the held hand device remainsin an active state to capture contextual information related to anobject area. The contextual information includes location basedinformation, timestamp information, traffic conditions, weatherconditions, area estimations and the like. The location basedinformation is captured using received signal strength indicator (RSSI)signals sensed from one or more hand held devices present within or inclose proximity of the object area.

At block 504, the contextual information related to an object area isanalyzed. The analysis of the contextual information is based onvehicular static or dynamic state interpreted from accelerometer data,and vehicle honking rate.

At block 506, the power intensive sensors are triggered to initiateaudio recording for extracting at least one feature after receiving theanalyzed contextual information. The variance of the accelerometer datais computed and compared against a threshold limit prior which is usedas to trigger the power intensive sensors into an active state.

At block 508, the corresponding metadata file is generated concurrentlywith step 506. The metadata file includes the contextual information.

At block 510, the extracted features and the metadata file aretransmitted to the backend server for analysis. The features and themetadata file are compressed and transmitted to the backend server forobtaining reduced data rate.

At block 512, after decompressing, the features and the metadata fileare mapped with the training models in backend server. In a preferredembodiment, the training models are preloaded in the backend server. Thetraining models are created using a Gaussian Mixture Model classifiersbased on audio analysis of horn and non horn sound detected from theobject area.

Although implementations for systems and methods an energy efficientmethod for participatory sensing for enabling at least one powereffective sensor to monitor operations of one or more power intensivesensors, it is to be understood that the appended claims are notnecessarily limited to the specific features or methods described.Rather, the specific features and methods are disclosed as examples ofimplementations for system and method for power effective participatorysensing.

We claim:
 1. An energy efficient method for participatory sensing,characterized in enabling at least one power effective sensor to monitoroperation of one or more power intensive sensors, wherein: the powereffective sensor remains in an active state to capture and analyzecontextual information related to an object area; in response toreceiving analyzed contextual information from the power effectivesensor, triggering the one or more power intensive sensors to initiateaudio recording for extracting at least one feature there from, andconcurrently generate a corresponding metadata file; and transmittingthe features and the metadata file for analysis to a backend server,wherein said features and the metadata file are analyzed for trackingthe object area condition.
 2. The energy efficient method of claim 1,wherein the contextual information comprises of location basedinformation, timestamp information, traffic conditions, weatherconditions, area estimations and the like.
 3. The energy efficientmethod of claim 2, wherein the location based information is capturedusing received signal strength indicator (RSSI) signals sensed from oneor more hand held devices present within or in close proximity of theobject area.
 4. The energy efficient method of claim 1, wherein theanalysis of the contextual information is based on vehicular static ordynamic state interpreted from accelerometer data, and vehicle honkingrate.
 5. The energy efficient method of claim 4, wherein variance of theaccelerometer data is computed and compared against a threshold limitprior to trigger the power intensive sensors into an active state. 6.The energy efficient method of claim 1, wherein the audio featureextraction is accomplished using a modified Mel Frequency CepstralCoefficient (MFCC) obtained by combining the conventional Mel filterstructure and reverse structure of conventional Mel filter.
 7. Theenergy efficient method of claim 1, further comprising compressing thefeatures and corresponding metadata at the hand held device anddecompressing the compressed features and the corresponding metadata atthe backend server.
 8. The energy efficient method of claim 1, whereinthe analysis at the backend server involves, mapping the features andthe corresponding metadata file with one or more pre-stored trainingmodels of the backend server.
 9. The energy efficient method of claim 8,wherein the training models are created using a Gaussian Mixture Modelclassifiers, based on audio analysis of horn and non horn sound detectedfrom the object area.
 10. The energy efficient method of claim 9,wherein the non horn sound includes non vehicular noise sound emanatingfrom engine idle noise, tire noise, air turbulence noise, human speech,vehicle music noise and the like.
 11. The energy efficient method ofclaim 1, wherein the object area condition refers to traffic conditionthereof.
 12. A participatory sensing system, comprising: a hand helddevice equipped with plurality of sensors, and configured to enable atleast one power effective sensor to monitor operation of one or morepower intensive sensors wherein, the power effective sensor remains inan active state to capture and analyze contextual information related toan object area; trigger the one or more power intensive sensors foraudio recording, in response to analyzed contextual information receivedfrom the power effective sensor; extract at least one feature from theaudio recording and concurrently generate a corresponding metadata file;and a backend server loaded with one or more training models that aremapped against the features and the metadata file received from the handheld device to track and monitor the object area condition.
 13. Theparticipatory sensing system of claim 12, wherein the power effectivesensors include an accelerometer, orientation sensor and the like, or acombination thereof.
 14. The participatory sensing system of claim 12,wherein the power intensive sensors include microphones; locationproviding sensors such as Global Positioning System (GPS); gyroscope andthe like, or a combination thereof.
 15. The participatory sensing systemof claim 12, wherein the contextual information comprises of locationbased information, timestamp information, traffic conditions, weatherconditions, area estimations and the like.
 16. The participatory sensingsystem of claim 12, wherein the power effective sensor analyses thecontextual information based on vehicular static or dynamic stateinterpreted from an accelerometer data, and vehicle-honking rate. 17.The participatory sensing system of claim 12, wherein the powerintensive sensors accomplishes feature extraction by using a modifiedMel Frequency Cepstral Coefficient (MFCC) obtained by combiningconventional Mel filter structure and reverse structure of conventionalMel filter.
 18. The participatory sensing system of claim 12, whereinthe hand held device is further configured to compress the features andcorresponding metadata, while the backend server is further configuredto decompress the compressed features and the corresponding metadataobtained from the hand held device.
 19. The participatory sensing systemof claim 12, wherein the training models are created using GaussianMixture Model classifiers based on audio analysis of horn and non hornsound detected from the object area.
 20. The participatory sensingsystem of claim 12, wherein the object area condition refers to trafficcondition thereof.